Datasets:
m_lama

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""The mLAMA Dataset"""


	import json
	import os

	import datasets


	_CITATION = """
	@article{kassner2021multilingual,
	author = {Nora Kassner and
	Philipp Dufter and
	Hinrich Sch{\"{u}}tze},
	title = {Multilingual {LAMA:} Investigating Knowledge in Multilingual Pretrained
	Language Models},
	journal = {CoRR},
	volume = {abs/2102.00894},
	year = {2021},
	url = {https://arxiv.org/abs/2102.00894},
	archivePrefix = {arXiv},
	eprint = {2102.00894},
	timestamp = {Tue, 09 Feb 2021 13:35:56 +0100},
	biburl = {https://dblp.org/rec/journals/corr/abs-2102-00894.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	note = {to appear in EACL2021}
	}
	"""


	_DESCRIPTION = """mLAMA: a multilingual version of the LAMA benchmark (T-REx and GoogleRE) covering 53 languages."""

	_HOMEPAGE = "http://cistern.cis.lmu.de/mlama/"

	_LICENSE = "The Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). https://creativecommons.org/licenses/by-nc-sa/4.0/"

	_URL = "http://cistern.cis.lmu.de/mlama/mlama1.1.zip"

	_LANGUAGES = (
	"af",
	"ar",
	"az",
	"be",
	"bg",
	"bn",
	"ca",
	"ceb",
	"cs",
	"cy",
	"da",
	"de",
	"el",
	"en",
	"es",
	"et",
	"eu",
	"fa",
	"fi",
	"fr",
	"ga",
	"gl",
	"he",
	"hi",
	"hr",
	"hu",
	"hy",
	"id",
	"it",
	"ja",
	"ka",
	"ko",
	"la",
	"lt",
	"lv",
	"ms",
	"nl",
	"pl",
	"pt",
	"ro",
	"ru",
	"sk",
	"sl",
	"sq",
	"sr",
	"sv",
	"ta",
	"th",
	"tr",
	"uk",
	"ur",
	"vi",
	"zh",
	)
	_RELATIONS = (
	"place_of_birth",
	"place_of_death",
	"P1001",
	"P101",
	"P103",
	"P106",
	"P108",
	"P127",
	"P1303",
	"P131",
	"P136",
	"P1376",
	"P138",
	"P140",
	"P1412",
	"P159",
	"P17",
	"P176",
	"P178",
	"P19",
	"P190",
	"P20",
	"P264",
	"P27",
	"P276",
	"P279",
	"P30",
	"P31",
	"P36",
	"P361",
	"P364",
	"P37",
	"P39",
	"P407",
	"P413",
	"P449",
	"P463",
	"P47",
	"P495",
	"P527",
	"P530",
	"P740",
	"P937",
	)


	class MLamaConfig(datasets.BuilderConfig):
	"""BuilderConfig for mLAMA."""

	def __init__(self, languages=None, relations=None, **kwargs):
	"""BuilderConfig for mLAMA.
	Args:
	languages: A subset of af,ar,az,be,bg,bn,ca,ceb,cs,cy,da,de,el,en,es,et,eu,fa,fi,fr,ga,gl,he,hi,hr,hu,hy,id,it,ja,ka,ko,la,lt,lv,ms,nl,pl,pt,ro,ru,sk,sl,sq,sr,sv,ta,th,tr,uk,ur,vi,zh
	relations: A subset of place_of_birth,place_of_death,P1001,P101,P103,P106,P108,P127,P1303,P131,P136,P1376,P138,P140,P1412,P159,P17,P176,P178,P19,P190,P20,P264,P27,P276,P279,P30,P31,P36,P361,P364,P37,P39,P407,P413,P449,P463,P47,P495,P527,P530,P740,P937
	**kwargs: keyword arguments forwarded to super.
	"""
	super(MLamaConfig, self).__init__(**kwargs)
	self.languages = languages if languages is not None else _LANGUAGES
	self.relations = relations if relations is not None else _RELATIONS


	class MLama(datasets.GeneratorBasedBuilder):
	"""multilingual LAMA Dataset (mLAMA)"""

	VERSION = datasets.Version("1.1.0")
	BUILDER_CONFIG_CLASS = MLamaConfig
	BUILDER_CONFIGS = [
	MLamaConfig(
	name="all",
	languages=None,
	relations=None,
	version=datasets.Version("1.1.0"),
	description="Import of mLAMA for all languages and all relations.",
	)
	]

	def _info(self):
	features = datasets.Features(
	{
	"uuid": datasets.Value("string"),
	"lineid": datasets.Value("uint32"),
	"obj_uri": datasets.Value("string"),
	"obj_label": datasets.Value("string"),
	"sub_uri": datasets.Value("string"),
	"sub_label": datasets.Value("string"),
	"template": datasets.Value("string"),
	"language": datasets.Value("string"),
	"predicate_id": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": os.path.join(data_dir, "mlama1.1"),
	"split": "test",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	"""Yields examples from the mLAMA dataset."""
	id_ = -1
	for language in self.config.languages:
	# load templates
	templates = {}
	with open(os.path.join(filepath, language, "templates.jsonl"), encoding="utf-8") as fp:
	for line in fp:
	line = json.loads(line)
	templates[line["relation"]] = line["template"]
	for relation in self.config.relations:
	# load triples
	with open(os.path.join(filepath, language, f"{relation}.jsonl"), encoding="utf-8") as fp:
	for line in fp:
	triple = json.loads(line)
	triple["language"] = language
	triple["predicate_id"] = relation
	triple["template"] = templates.get(relation, "")
	id_ += 1
	yield id_, triple